¹Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, 32026, Taiwan, R.O.C. E-Mail: iebjiang@saturn.yzu.edu.tw

² Department of Industrial Engineering and Management, Ching Yun Institute of Technology, Taiwan, R.O.C.

 
ABSTRACT:
A manufacturability evaluation decision model is formulated and analyzed based on fuzzy logic and multiple attribute decision-making under the concurrent engineering environment. The study emphasizes on the treatment of the linguistic and vagueness at the early product development stage.  The study also considers the function integration of the total life cycle of a product. Hence, the integrated decision model covers the multi-level, multi-goal requirements of the products.  Multiple criteria such as the goal space, the decision space, the function space, the development (i.e., product & process design) space, and the activity space, are then applied under different analysis of decision-making methods. For instances, the fuzzy multiple attribute decision-making (FMADM) combined with activity-based costing (ABC) can be used in the activity decision space. The fuzzy logic decision model can be applied in the goal decision space. The results of this study point out the importance of early decision making capability. An example of a high-pressure vessel is provided to demonstrate the proposed model for evaluating the manufacturability.

KEY WORDS: product development, concurrent engineering, fuzzy logic, multiple attribute decision making.

Department of Industrial Engineering and Management, Yuan Ze University, Chung-Li, 32026, Taiwan, R.O.C. E-Mail：cytsai@saturn.yzu.edu.tw

ABSTRACT:
In the conceptual design stage, designers usually initiate a design concept through an association activity.  The activity helps designers collect and retrieve reference information regarding a current design subject instead of starting from scratch.  By modifying previous designs, designers can create a new design in a much shorter time.  To computerize this process, this paper proposes an intelligent design retrieval system involving soft computing techniques for both feature and object association functions.  A feature association method that utilizes fuzzy relation and fuzzy composition is developed to increase the searching spectrum.  In the mean time, object association functions composed by a fuzzy neural network allow designers to control the similarity of retrieved designs.  Our implementation result shows that the intelligent design retrieval system with two soft computing based association functions can retrieve target reference designs as expected.

KEY WORDS: soft computing, fuzzy set theory, neural networks, manufacturing features, and intelligent design retrieval.

School of Mechanical and Production Engineering
Nanyang Technological University
50 Nanyang Avenue, Singapore 639798
*mlpkhoo@ntu.edu.sg

ABSTRACT:
Over the past decade, much work has been done to optimise assembly process plans to improve productivity. Among them, Genetic Algorithms (GAs) are one of the most widely used techniques. Basically, GAs are optimisation methodologies based on a direct analogy to Darwinian natural selection and genetics in biological systems. They can deal with complex product assembly planning. However, during the process, the neighbourhood may converge too fast and limit the search to a local optimum prematurely. In a similar domain, Tabu Search (TS) constitutes a meta-procedure that organises and directs the operation of a search process. It is able to systematically impose and release constraints so as to permit the exploration of otherwise forbidden regions in a search space. This study attempts to combine the strengths of GAs and TS to realize a hybrid approach for optimal assembly process planning. More robust search behaviour can possibly be obtained by incorporating the Tabu’s intensification and diversification strategies into GAs. The hybrid approach also takes into account assembly guidelines and assembly constraints in the derivation of near optimal assembly process plans. A case study on a cordless telephone assembly is used to demonstrate the approach. Results show that the assembly process plans obtained are superior to those derived by GA alone. The details of the hybrid approach and the case study are presented.
KEY WORDS: Genetic Algorithms, Tabu Search, Assembly guidelines, Assembly constraints.

 KEY WORDS: Hot Isostatic Pressing (HIP), Artificial Neural Networks, Powder Metallurgy, Near Net-Shape.

²Department of Business Administration
Minghsin Institute of Technology, Hsinchu, Taiwan, R.O.C.


ABSTRACT:
A critical aspect of wire bonding is the quality of the bonding strength that contributes the major part of yield loss to the integrated circuit assembly process. This paper applies an integrated approach using a neural networks and genetic algorithms to optimize IC wire bonding process. We first use a back-propagation network to provide the nonlinear relationship between factors and the response based on the experimental data from a semiconductor manufacturing company in Taiwan. Then, a genetic algorithms is applied to obtain the optimal factor settings. A comparison between the proposed approach and the Taguchi method was also conducted. The results demonstrate the superiority of the proposed approach in terms of process capability.

KEY WORDS: Integrated circuit (IC), Wire bonding, Neural networks, Back-propagation network, Genetic algorithms.

Intelligent Remote Monitoring and Diagnosis of Manufacturing Processes Using 

Journal of Intelligent Manufacturing, Vol. xx, No. x, pp. xxx-xxx, 2002.

by Tung-Hsu (Tony) Hou*, Wang-Lin Liu* and Li Lin**

* Institute of Industrial Engineering and Management
National Yunlin University of Science & Technology, Taiwan, R.O.C.
** Department of Industrial Engineering
University at Buffalo, State University of New York, U.S.A.

ABSTRACT:
This research develops a methodology for the intelligent remote monitoring and diagnosis of manufacturing processes. A back propagation neural network monitors a manufacturing process and identifies faulty quality categories of the products being produced. For diagnosis of the process, rough set is used to extract the causal relationship between manufacturing parameters and product quality measures. Therefore, an integration of neural networks and a rough set approach not only provides information about what is expected to happen, but also reveals why this has occurred and how to recover from the abnormal condition with specific guidelines on process parameter settings. The methodology is successfully implemented in an Ethernet network environment with sensors and PLC connected to the manufacturing processes and control computers. In an application to a manufacturing system that makes conveyor belts, the back propagation neural network accurately classified quality faults, such as wrinkles and uneven thickness. The rough set also determined the causal relationships between manufacturing parameters, e.g., process temperature, and output quality measures. In addition, rough set provided operating guidelines on specific settings of process parameters to the operators to correct the detected

KEY WORDS: Remote monitoring, Computer networks, Neural networks, Data mining, Rough set.